Generation of vital sign monitoring

ABSTRACT

Electrical impulses are received from a beating heart. The electrical impulses are converted to an ECG waveform. The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients or to a set of rules developed from the database. A bundle branches (BB) to J-Point (BB-J) interval is identified from the plurality of subwaveforms and discontinuity points based on the comparison. The ECG waveform with the BB-J interval annotated is displayed.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation in part of U.S. patent applicationSer. No. 16/561,304, filed Sep. 5, 2019, which is a continuation in partof U.S. patent application Ser. No. 16/157,178, filed Oct. 11, 2018,which is a continuation in part of U.S. patent application Ser. No.16/114,025, filed Aug. 27, 2018, which is a continuation in part of U.S.patent application Ser. No. 15/998,487, filed Aug. 16, 2018, which is acontinuation in part of U.S. patent application Ser. No. 15/961,952,filed Apr. 25, 2018, now U.S. Pat. No. 10,092,201, which is acontinuation in part of U.S. patent application Ser. No. 15/904,543,filed Feb. 26, 2018, now U.S. Pat. No. 10,085,663, which is acontinuation in part of U.S. patent application Ser. No. 15/393,135,filed Dec. 28, 2016, now U.S. Pat. No. 9,999,364, which is acontinuation in part of U.S. patent application Ser. No. 14/749,697,filed Jun. 25, 2015, now U.S. Pat. No. 9,538,930 (hereinafter the “'930Patent”), which is a continuation in part of U.S. patent applicationSer. No. 14/662,996, filed Mar. 19, 2015, now U.S. Pat. No. 9,339,204(hereinafter the “'204 Patent”), which is a continuation of PCTApplication No. PCT/US15/20828, filed Mar. 16, 2015, which claims thebenefit of U.S. Provisional Patent Application Ser. No. 62/008,435,filed Jun. 5, 2014; this application also claims the benefit of U.S.Provisional Patent Application Ser. No. 62/742,477, filed Oct. 8, 2018,this application also claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/727,028, filed Sep. 5, 2018; U.S. patentapplication Ser. No. 16/157,178 claims the benefit of U.S. ProvisionalPatent Application Ser. No. 62/571,180, filed Oct. 11, 2017 and U.S.Provisional Patent Application Ser. No. 62/701,841, filed Jul. 23, 2018;U.S. patent application Ser. No. 16/114,025 claims the benefit of U.S.Provisional Patent Application Ser. No. 62/551,759, filed Aug. 29, 2017;U.S. patent application Ser. No. 15/998,487 claims the benefit of U.S.Provisional Patent Application Ser. No. 62/546,461, filed Aug. 16, 2018;U.S. patent application Ser. No. 15/961,952 claims the benefit of U.S.Provisional Patent Application Ser. No. 62/489,540, filed Apr. 25, 2017;U.S. patent application Ser. No. 15/904,543 claims the benefit of U.S.Provisional Patent Application Ser. No. 62/463,662, filed Feb. 26, 2017;U.S. Patent Application Serial No. application Ser. No. 15/393,135claims the benefit of U.S. Provisional Patent Application Ser. No.62/271,704, filed Dec. 28, 2015, and U.S. Provisional Patent ApplicationSer. No. 62/271,699, filed Dec. 28, 2015; and U.S. patent applicationSer. No. 14/749,697 claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/017,185, filed Jun. 25, 2014, the content of allof which is incorporated by reference herein in their entireties.

INTRODUCTION

The teachings herein relate to vital sign monitoring using an automatedelectrocardiography (ECG) analysis system. More particularly, theteachings herein relate to systems and methods for displaying andmeasuring intervals and segments of an ECG waveform during measurementof the ECG waveform. These systems and methods use the harmonic signalsof a conventional ECG waveform and previously recorded data from normaland abnormal patients or rules developed from the previously recordeddata to identify a bundle branches (BB) to J-Point (BB-J) interval of anECG waveform.

The systems and methods herein can be performed in conjunction with aprocessor, controller, or computer system, such as the computer systemof FIG. 1.

BACKGROUND

Traditional Vital Sign Monitoring

Since its invention in 1903, the ECG device has not significantlychanged. Abnormalities are sometimes displayed as normal and normalresults are sometimes displayed as abnormal, making it almost impossiblefor doctors and nurses to correctly recognize and diagnose disease. Themain reason for this ambiguity is the abstract self-similarity of theexisting ECG waveform.

The ECG waveform is the most important parameter of Vital SignMonitoring. Blood Pressure (NIBP) and Oxygen Saturation (SpO2) aresecondary parameters. Death is mainly caused by cardiac changes withinthe heart, even when there is no disease evident. The cardiacinformation changes quickly from physiological to pathological. However,the current Vital Signs Monitor is only a warning device after theoccurrence of critical events.

Also, many myocardial ischemia events do not even trigger an alarm.Myocardial infarction does not trigger an alarm. Heart failure does nottrigger an alarm. Serious conduction block does not trigger an alarm.Serious coronary heart disease does not trigger an alarm. AMI, ACS, andso on, all do not trigger an alarm in advance, leading to risk factorsthat lead to death when the alarm is finally triggered. This representsan imperative on the traditional vital sign monitoring of cardiacmonitor.

In current Vital Sign Monitoring, when patients appear to show signs ofVentricular Tachycardia (VT), Ventricular Fibrillation (VF) or acutecardiac events, it is already too late. The existing ECG detection rateof coronary heart disease is only 17˜25%. Only half of patients withacute myocardial infarction with impairment greater than 70% can bedetected. This is because a traditional ECG device does not display STsegment changes in elevation or descent accurately. In the early stagesof cardiac ischemia, if immediate action is not taken, the monitor losesits basic functions.

As a result, there is a need for Vital Sign Monitoring ECG systems andmethods that provide automatic detection of possible disease and nolonger simply act as an alarm device after the occurrence of criticalevents.

ECG Quantitative Problem

Since the development of ECG, the electrophysiological signal separationof specific time periods of cardiac self-conduction system within the Pwave, the QRS complex and the T wave has never been achieved. There isstandardized data available in cardiac science, but such quantitativedata has never been used in ECG for many reasons. Conventionally, onlythe character of an ECG waveform is read. As a result, only qualitativeinformation is provided in clinical applications.

ECG is a noninvasive electrophysiological technology. It is able to scanand record cardiac electrical conduction signals. It is the only tracingimage of cardiac “bioelectric conduction”, which is the character markerof the life of a heart. However, in the medical field, ECG is only amorphological technique, and the target of its reading, analyzing anddetermining is waveform character. It is a qualitative technology thatdoes not have quantitative data, which is due to the ECG waveform beingassociated with deformation and instability. The standard measuringpoints frequently disappear, hide, overlap and shift. Consequently, theyare frequently not shown in ECG, and thus cannot be used for making adetermination.

Moreover, conventional ECG cannot measure all digital parameters. Theestablished standards cannot be applied clinically, and cannot bemeasured. As a result, ECG is unable to achieve a data-basedquantitative application. Hence, to date, ECG is still a qualitativeapplication. The foregoing is the reason for which ECG is deemed as atechnology that needs experience. However, it is noted that suchexperience needs to be accumulated from a great number of cases withincorrect diagnosis.

The heart is the most important organ in human body. In addition toheart diseases, a variety of other diseases may also cause abnormalitiesin heart. Hence, not only cardiologists, but also all doctors in otherdepartments need to read ECG. However, the morphological characterchanges in ECG cause difficulties for doctors to read ECG. Hence, theissue of how to use the ECG data has confused clinical practices formany years. In traditional ECG, diagnosis of diseases is still madeaccording to morphological character changes in waveforms.

As a result, systems and methods are needed to add quantitativescientific indicators to ECG. Such systems and methods are needed toallow doctors to understand and utilize the knowledge saved intraditional ECG, as well as to reduce learning difficulties, reduceguesses in case diagnosis, reduce misjudgment rate, and improvereliability, diagnosis rate and accuracy, whenever waveform characterchanges.

ECG Accuracy Problem

Since the first ECG instrument was invented in 1903, its accuracy ratefor diagnosis has always been a problem in clinical applications. Forpeople with abnormal conditions, ECG waveform variations are not thesame for the same person and are not completely identical even for thesame disease. They are at most self-similar. Self-similarly, forexample, refers to an object having a shape that is similar to the shapeof one of its parts. As a result, ECG science is one of the mostcomplicated disciplines in medicine.

It can be seen from numerous signal processing methods that, during alifetime, each beat of a person's heart has different specific signalvariation, and the difference is significant. However, generally one isunable to observe this from a conventional linear ECG waveform with thenaked eye.

Since computers started to be widely used in ECG analysis the 1970s,people have been consistently exploring, searching, and studying how toautomate ECG analysis and diagnosis. In the past half a century,thousands of scholars have made efforts in studying algorithms,exploring pattern recognition, and applying those in ECG mapping andautomatic diagnosis.

However, wide clinical use of such systems has yet to be achieved. Thereare at least three technical reasons for this.

1. The ECG waveform is morphological, and generally no consistentmapping points can be found. In other words, the information in the ECGwaveform is conveyed through its structure or form. Also, the waveformis abstractly self-similar. In particular, there is no rule for abnormalvariations, the time axis signals interfere with each other on left andright sides of as well as above the x-axis, non-linear variations areinvisible, and the same disease may have hundreds of millions ofvariations, but they are not clearly displayed on the ECG waveform. As aresult, all ECG parameters are, in general, not accurate, and it isalmost impossible to measure these parameters after the waveformchanges. Therefore, the highest accuracy of automatic diagnosis byexisting ECG software reaches around 38%. Also, this accuracy is onlyachieved for simple ECG waveform variations and not for many complexwaveforms. This is because no mapping point can be found due to the lossor disappearance or deformation of the P-QRS-T waveform.

2. The second reason systems for automated ECG analysis and diagnosishave not been adopted clinically is related to how a conventional ECGwaveform has been measured. As described in the '204 Patent and below,the conventional ECG waveform is a single time domain waveform thatrepresents a combination of many different frequency domain signals fromdifferent parts of the heart muscle. As a result, information specificto these different parts of the heart muscle are generally lost. Inaddition, the conventional ECG waveform is a linear waveform, while theheart is a nonlinear system, and the vast majority of variations as aresult of abnormality are nonlinear.

3. The third reason systems for automated ECG analysis and diagnosishave not been adopted clinically is related to the high number of falsepositives found in normal and abnormal populations. For example, in manycases, conventional ECG waveforms show abnormal results in tests ofnormal people and also show normal results in tests of abnormal people,which makes it extremely difficult for clinical reading andunderstanding and makes it impossible to determine whether a result isnormal or abnormal.

However, the heart is an electrified organ, and there is no doubt thatthe electrophysiological responses of a heart organ are the fastest andmost sensitive measurements to diagnose heart problems. ECG remains oneof the most extensively used clinical tools used at present along withblood tests and imaging, despite the lack of accurate systems forautomated ECG analysis and diagnosis. As a result, there is asignificant need for such systems.

Recent advancements have addressed one of the three technical problems.This is the conventional ECG waveform measurement problem. As describedin the '204 Patent and below, an ECG device has been developed that usessignal processing to detect one or more subwaveforms within the P, Q, R,S, T, U, and J waveforms of a conventional ECG waveform and/or withinthe intervals between the P, Q, R, S, T, U, and J waveforms of aconventional ECG waveform. In other words, the device of the '204 Patentcan provide information (subwaveforms) about different frequency domainsignals from different parts of the heart muscle. A waveform displayingthese subwaveforms is referred to as a saah ECG waveform, for example.In FIG. 30, described below, portions of a saah ECG waveform 3030 and aconventional or traditional ECG waveform 3040 are compared. FIG. 30shows that saah ECG waveform 3030 relates ECG signals more closely tothe anatomy of self-conducting system 3020 than traditional ECG waveform3040.

As described in the '930 Patent and below, one way the differentfrequency domain signals from different parts of the heart muscle can bemeasured is through multi-domain ECG. In multi-domain ECG heart signalsare measured using different frequency bands. These multi-domain ECGheart signals can be displayed in one diagram as anelectrophysiocardiogram (EPCG) waveform. FIG. 32 shows EPCG waveformsbefore and after percutaneous coronary intervention (PCI), for example.

As a result of the systems of the '204 Patent and the '930 Patent, thetechnical problem of measuring the different frequency domain signalsfrom different parts of the heart muscle has been addressed.

ECG Parameter Measurement Problem

The heart beats day and night from the first day of a human life to thelast day of life. In a whole life, the heart beats about 2.5 billion to3 billion times. In this regard, it could be calculated that the hearteach time pumps 80 ml blood. Accordingly, based on the fact that theheart beats about 70 times per minute on average, the heart hence pumps8,000 liters of blood per day, which is equivalent to the volume of 40barrels of gasoline, and the total weight would be 8 tons. Therefore,the heart pumps 3,000 tons of blood per year. If a person lives for 80years, the number will reach 240,000 tons. It is noted that after theage of 60 years old, a person has a 45% chance of having the conditionof arrhythmia, and about half of those cases become life threatening.According to a variety of different scientific predictions, somescientists believe that it is reasonable to predict that the averagelifespan of a person is about 80 years old.

The heart is a charged elastic mucus organ, so an ECG instrument is theonly instrument that is able to scan and record the physiological andpathological signs of the cardiac electrophysiology (heart ultrasoundprovides hemodynamic data, CT & MIR provide histological imaging data).ECG provides electrophysiological signals, in particular noninvasiveelectrophysiological data. It is able to scan and record cardiacelectrical conduction signals. By far, it is the only tool that canrecord the scanning image of “bioelectric conduction,” the identifier ofa living heart.

On the other hand, however, in the medical field, ECG is also only amorphological signal. It needs to be read and analyzed to determine itsvarious waveforms. In this regard, it is an area that highly relies on apractitioner's experience. In the history of ECG, there are manydata-based parameter gold standards, such as P-R interval, Q-T interval,ST segment, QRS complex, P-J interval, J-T interval, VAT.

However, due to the fact that ECG waveforms are prone to certain issuessuch as deformation, instability, standard point loss, and so on,conventional ECG instruments are unable to accurately measure these ECGparameters. As a result, many established standards cannot be applied inclinical practice.

Furthermore, as for the data measured manually, a very small variationcan result in a difference of tens of milliseconds. Clinically, onlysimple standards can be used at present, such as: HR, RR interval, PPinterval. As a result, only a few very simple standards can be used incurrent clinical practice, including HR, RR interval, PP interval, etc.

As described above, the ECG systems of the '204 Patent and the '930Patent have addressed the technical problem of accurately measuring thedifferent frequency domain signals from different parts of the heartmuscle.

Additional systems and methods, however, are needed to accuratelymeasure ECG parameters such as the P-R interval, Q-T interval, STsegment, QRS complex, P-J interval, J-T interval, and VAT so that thesestandards can be used in clinical practice.

ECG History

Electrical signals produced by a human heart were observed throughelectrodes attached to a patient's skin as early as 1879. Between 1897and 1911 various methods were used to detect these electrical signalsand record a heartbeat in real-time. In 1924, Willem Einthoven wasawarded the Nobel Prize in medicine for identifying the variouswaveforms of a heartbeat and assigning the letters P, Q, R, S, T, U, andJ to these waveforms. Since the early 1900s, the equipment used forelectrocardiography (ECG or EKG) has changed. However, the basicwaveforms detected and analyzed have not changed.

An ECG device detects electrical impulses or changes in the electricalpotential between two electrodes attached to the skin of a patient asthe heart muscle contracts or beats. Electrically, the contraction ofthe heart is caused by depolarization and repolarization of variousparts of the heart muscle. Initially, or at rest, the muscle cells ofthe heart have a negative charge. In order to cause them to contract,they receive an influx of positive ions Na⁺ and Ca⁺⁺. This influx ofpositive ions is called depolarization. The return of negative ions tobring the heart back to a resting state is called repolarization.Depolarization and repolarization of the heart affect different parts ofthe heart over time giving rise to the P, Q, R, S, T, U, and Jwaveforms.

FIG. 2 is an exemplary plot 200 of the P, Q, R, S, and T waveforms of aconventional ECG waveform of a heartbeat from a conventional ECG device.The P, Q, R, S, and T waveforms represent electrical conduction througha heart muscle. P waveform 210 represents the propagation ofdepolarization from the sinoatrial node, to the right and left atriums,and to the atrioventricular node. The sinoatrial node is also referredto as the sinus node, SA node, or SAN. The atrioventricular node is alsoreferred to as the AV node or AVN. The right atrium is also referred toas the RA, and the left atrium is also referred to as the LA.

FIG. 3 is an exemplary diagram 300 of the depolarization of the muscletissue of a heart that produces P waveform 210 of FIG. 2 as detected bya conventional ECG device. P waveform 210 of FIG. 2 is produced asdepolarization propagates from SAN 310 to AVN 340 in FIG. 3. Asdepolarization propagates from SAN 310 to AVN 340, it also spreads fromRA 320 to LA 340. P waveform 210 of FIG. 2 typically has a duration of80 ms, for example.

PR segment 220 of FIG. 2 represents the propagation of depolarizationfrom the AVN to the Bundle of His, and then to the Bundle Branches. PRsegment 230 may also include depolarization to the Purkinje fibers ofthe inner ventricular walls. The Bundle of His is also referred to asthe His Bundle or His. The Bundle Branches include the right bundlebranches (RBB) and the left bundle branches (LBB). As shown in FIG. 2,in a conventional ECG, PR segment 220 shows up as a flat line orwaveform with no amplitude.

FIG. 4 is an exemplary diagram 400 of the depolarization of the muscletissue of a heart that produces PR segment 220 of FIG. 2 as detected bya conventional ECG device. PR segment 220 of FIG. 2 is produced asdepolarization propagates from AVN 340 to His 450 and then to BundleBranches 460 that include RBB 461 and LBB 462. PR segment 220 of FIG. 2typically has a duration of between 50 and 120 ms, for example.

Waveforms Q 230, R 240, and S 250 of FIG. 2 form the QRS complex. TheQRS complex represents the propagation of depolarization through theright and left ventricles. The right ventricle is also referred to asRV, and the left ventricle is referred to as LV.

FIG. 5 is an exemplary diagram 500 of the depolarization of the muscletissue of a heart that produces Q waveform 230, R waveform 240, and Swaveform 250 of FIG. 2 as detected by a conventional ECG device.Waveforms Q 230, R 240, and S 250 of FIG. 2 produced as depolarizationpropagates from Bundle Branches 460 through RV 571 and LV 572. RV 571and LV 572 have the largest muscle mass in the heart. The QRS complexformed by waveforms Q 230, R 240, and S 250 of FIG. 2 typically has aduration of between 80 and 100 ms, for example.

ST segment 260 of FIG. 2 represents the period during which theventricles remain depolarized and contracted. As shown in FIG. 2, in aconventional ECG, ST segment 260 shows up as a flat line or waveformwith no amplitude. ST segment 260 typically has a duration of between 80and 120 ms, for example.

The point in FIG. 2 at which the QRS complex ends and ST segment 260begins is called J point 255. A J waveform (not shown) can sometimesappear as an elevated J point at J point 255 or as a secondary Rwaveform. A J waveform is usually characteristic of a specific disease.The J waveform is also referred to as the Osborn wave, camel-hump sign,late delta wave, hathook junction, hypothermic wave, prominent J wave, Kwave, H wave or current of injury.

T waveform 270 of FIG. 2 represents the repolarization or recovery ofthe ventricles. T waveform 270 typically has a duration of 160 ms, forexample. The interval between the Q and T waveforms is referred to asthe QT interval.

FIG. 6 is an exemplary diagram 600 of the repolarization of the muscletissue of a heart that produces T waveform 270 of FIG. 2 as detected bya conventional ECG device. As shown in FIG. 6, RV 571 and LV 572 arerepolarized.

Not shown in FIG. 2 is the U waveform. The U waveform sometimes appearsafter the T waveform. The U waveform is thought to representrepolarization of the interventricular septum, the papillary muscles, orthe Purkinje fibers.

As shown in FIGS. 3 through 6, as a heart beats, electrical signals flowthrough all the different muscle tissues of the heart. As shown in FIG.2, for the last 100 years conventional ECG devices have been able todetect some of these signals in the form of the P, Q, R, S, T, U, and Jwaveforms. These waveforms have aided in the diagnosis and treatment ofmany heart problems. Unfortunately, however, the P, Q, R, S, T, U, and Jwaveforms do not provide a complete picture of the operation of all thedifferent muscle tissues of the heart. As a result, improved systems andmethods are needed to detect and analyze more information from theelectrical signals that flow through all the different muscle tissues ofthe heart as it is beating. This additional information can be used todiagnose and treat many more heart problems.

Artificial Intelligence

Artificial Intelligence (AI) generally refers to languages, algorithms,and operating systems that relate to how a computer system can carry outtasks that were previously only completed by relying on humanintelligence. It is a general term and often does not includeimplementation or application. The definition of AI has evolved overtime, however, and this phenomenon is referred to as the “AI effect.”The AI effect can be summarized as the prescription that “AI intends tocomplete a collection of all tasks that cannot be implemented withoutrelying on human intelligence at the present.” In the 1940s and 1950s, agroup of scientists from different fields (mathematics, psychology,engineering, economics and politics) began to explore the possibility ofmanufacturing an artificial brain. In 1956, AI was established as adiscipline. The organizers of the 1956 Dartmouth Artificial IntelligenceConference were Marvin Minsky, John McCarthy, and two other seniorscientists, Claude Shannon and Nathan Rochester, with the latter comingfrom IBM. At the 1956 Dartmouth Artificial Intelligence Conference, thename and tasks of AI were determined, and at the same time, initialachievements and the earliest group of researchers appeared. As aresult, this event has been extensively acknowledged as a sign of thebirth of AI. It is clear that AI is now a technological field, a secondrevolution since the invention of the computer, and a certain trend inthe future. It is being applied in all industries, exists everywhere,and is used on almost everything on the earth. In the medical field, AIis now used in the following: medical imaging, sensor-based dataanalysis, conversion of bioinformatics, and development of public healthpolicies. AI is also used in the clinical applications. Theseapplications include cancer treatment: recognition of mitosis ofcancerous tumor cells, identification of disease types and degrees ofaggravation, shortening chemotherapy time, and mitigating damage causedby chemotherapy for cancer patients. These applications also includeophthalmological diagnosis: recognition of early signs of eye disease,such as senile macular degeneration, and diabetic retinopathy andsurgical treatment: AI surgical robots, etc. Google has also formed ateam called DeepMind Health, which cooperated with the Imperial CollegeLondon and the Royal Free Hospital in London, UK. They released a mobileapplication called Streams, and medical professionals can use Streams toobserve treatment results in a faster manner. Overall, in the medicalfield, the AI system can be used on any job that previously requiredhuman thinking.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates a computer system, inaccordance with various embodiments.

FIG. 2 is an exemplary plot of the P, Q, R, S, and T waveforms of aconventional electrocardiography (ECG) waveform of a heartbeat from aconventional ECG device.

FIG. 3 is an exemplary diagram of the depolarization of the muscletissue of a heart that produces the P waveform of FIG. 2 as detected bya conventional ECG device.

FIG. 4 is an exemplary diagram of the depolarization of the muscletissue of a heart that produces the PR segment of FIG. 2 as detected bya conventional ECG device.

FIG. 5 is an exemplary diagram of the depolarization of the muscletissue of a heart that produces the Q waveform, the R waveform, and theS waveform of FIG. 2 as detected by a conventional ECG device.

FIG. 6 is an exemplary diagram of the repolarization of the muscletissue of a heart that produces the T waveform of FIG. 2 as detected bya conventional ECG device.

FIG. 7 is a block diagram of a conventional ECG device.

FIG. 8 is a block diagram of an ECG device for detecting moreinformation from the electrical signals that flow through all thedifferent muscle tissues of the heart as it is beating, in accordancewith various embodiments.

FIG. 9 is an exemplary plot of a saah ECG waveform of a heartbeat from asaah ECG device showing subwaveforms found within the P, Q, R, S, T, U,and J waveforms and/or within the intervals between the P, Q, R, S, T,U, and J waveforms, in accordance with various embodiments.

FIG. 10 is an exemplary block diagram showing a signal processingalgorithm for detecting five subwaveforms within the PR interval of aconventional ECG waveform, in accordance with various embodiments.

FIG. 11 is an exemplary block diagram of a saah ECG device that displaysconventional ECG waveforms, saah ECG waveforms, and saah ECG data, inaccordance with various embodiments.

FIG. 12 is an exemplary plot of the information displayed by the saahECG device of FIG. 10, in accordance with various embodiments.

FIG. 13 is an exemplary block diagram of a saah ECG device that displaysconventional ECG waveforms, saah ECG waveforms, saah ECG data, and saahECG automatic pattern recognition diagnosis information, in accordancewith various embodiments.

FIG. 14 is an exemplary plot of a conventional ECG waveform showing howa small error in the starting point of the P wave can cause a largeerror in all subsequent time measurements, in accordance with variousembodiments.

FIG. 15 is an exemplary plot of a conventional ECG waveform showing howthe end of the PR interval cannot be confirmed due to an upward arcangle of the starting point of QRS wave, in accordance with variousembodiments.

FIG. 16 is a flowchart showing a method for detecting subwaveformswithin the P, Q, R, S, T, U, and J waveforms of an ECG waveform of aheartbeat or in an interval between the P, Q, R, S, T, U, and Jwaveforms, in accordance with various embodiments.

FIG. 17 is a schematic diagram of a system that includes one or moredistinct software modules that perform a method for detectingsubwaveforms within the P, Q, R, S, T, U, and J waveforms of an ECGwaveform of a heartbeat or in an interval between the P, Q, R, S, T, U,and J waveforms, in accordance with various embodiments.

FIG. 18 is an exemplary block diagram of a system for automated ECGanalysis and diagnosis using AI, in accordance with various embodiments.

FIG. 19 is a block diagram of an ECG system for identifying andannotating cardiac electrophysiological signals in an ECG waveform asnormal or abnormal during measurement of the ECG waveform, in accordancewith various embodiments.

FIG. 20 is an exemplary diagram of a new ECG waveform and acorresponding traditional ECG waveform, in accordance with variousembodiments.

FIG. 21 is an exemplary diagram of an ECG diagnostic color separationwaveform, in accordance with various embodiments.

FIG. 22 is an exemplary diagram of a new ECG waveform and acorresponding traditional ECG waveform, showing some additionalparameter timing values, in accordance with various embodiments.

FIG. 23 is an exemplary table showing the timing parameter values of theECG waveform in FIG. 22, including new timing parameter values, inaccordance with various embodiments.

FIG. 24 is an exemplary diagram showing how heart signal drift isfiltered, in accordance with various embodiments.

FIG. 25 is a flowchart showing a method for displaying and measuringintervals and segments of an ECG waveform during measurement of the ECGwaveform, in accordance with various embodiments.

Before one or more embodiments of the invention are described in detail,one skilled in the art will appreciate that the invention is not limitedin its application to the details of construction, the arrangements ofcomponents, and the arrangement of steps set forth in the followingdetailed description. The invention is capable of other embodiments andof being practiced or being carried out in various ways. Also, it is tobe understood that the phraseology and terminology used herein is forthe purpose of description and should not be regarded as limiting.

DETAILED DESCRIPTION

Computer-Implemented System

FIG. 1 is a block diagram that illustrates a computer system 100, uponwhich embodiments of the present teachings may be implemented. Computersystem 100 includes a bus 102 or other communication mechanism forcommunicating information, and a processor 104 coupled with bus 102 forprocessing information. Computer system 100 also includes a memory 106,which can be a random-access memory (RAM) or other dynamic storagedevice, coupled to bus 102 for storing instructions to be executed byprocessor 104. Memory 106 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 104. Computer system 100further includes a read only memory (ROM) 108 or other static storagedevice coupled to bus 102 for storing static information andinstructions for processor 104. A storage device 110, such as a magneticdisk or optical disk, is provided and coupled to bus 102 for storinginformation and instructions.

Computer system 100 may be coupled via bus 102 to a display 112, such asa cathode ray tube (CRT) or liquid crystal display (LCD), for displayinginformation to a computer user. An input device 114, includingalphanumeric and other keys, is coupled to bus 102 for communicatinginformation and command selections to processor 104. Another type ofuser input device is cursor control 116, such as a mouse, a trackball orcursor direction keys for communicating direction information andcommand selections to processor 104 and for controlling cursor movementon display 112. This input device typically has two degrees of freedomin two axes, a first axis (i.e., x) and a second axis (i.e., y), thatallows the device to specify positions in a plane.

A computer system 100 can perform the present teachings. Consistent withcertain implementations of the present teachings, results are providedby computer system 100 in response to processor 104 executing one ormore sequences of one or more instructions contained in memory 106. Suchinstructions may be read into memory 106 from another computer-readablemedium, such as storage device 110. Execution of the sequences ofinstructions contained in memory 106 causes processor 104 to perform theprocess described herein. Alternatively, hard-wired circuitry may beused in place of or in combination with software instructions toimplement the present teachings. Thus, implementations of the presentteachings are not limited to any specific combination of hardwarecircuitry and software.

In various embodiments, computer system 100 can be connected to one ormore other computer systems, like computer system 100, across a networkto form a networked system. The network can include a private network ora public network such as the Internet. In the networked system, one ormore computer systems can store and serve the data to other computersystems. The one or more computer systems that store and serve the datacan be referred to as servers or the cloud, in a cloud computingscenario. The other computer systems that send and receive data to andfrom the servers or the cloud can be referred to as client or clouddevices, for example.

The term “computer-readable medium” as used herein refers to any mediathat participates in providing instructions to processor 104 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media includes, for example, optical or magnetic disks,such as storage device 110. Volatile media includes dynamic memory, suchas memory 106. Transmission media includes coaxial cables, copper wire,and fiber optics, including the wires that comprise bus 102.

Common forms of computer-readable media or computer program productsinclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, or any other magnetic medium, a CD-ROM, digital videodisc (DVD), a Blu-ray Disc, any other optical medium, a thumb drive, amemory card, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memorychip or cartridge, or any other tangible medium from which a computercan read.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to processor 104 forexecution. For example, the instructions may initially be carried on themagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 100 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detectorcoupled to bus 102 can receive the data carried in the infra-red signaland place the data on bus 102. Bus 102 carries the data to memory 106,from which processor 104 retrieves and executes the instructions. Theinstructions received by memory 106 may optionally be stored on storagedevice 110 either before or after execution by processor 104.

In accordance with various embodiments, instructions configured to beexecuted by a processor to perform a method are stored on acomputer-readable medium. The computer-readable medium can be a devicethat stores digital information. For example, a computer-readable mediumincludes a compact disc read-only memory (CD-ROM) as is known in the artfor storing software. The computer-readable medium is accessed by aprocessor suitable for executing instructions configured to be executed.

The following descriptions of various implementations of the presentteachings have been presented for purposes of illustration anddescription. It is not exhaustive and does not limit the presentteachings to the precise form disclosed. Modifications and variationsare possible in light of the above teachings or may be acquired frompracticing of the present teachings. Additionally, the describedimplementation includes software but the present teachings may beimplemented as a combination of hardware and software or in hardwarealone. The present teachings may be implemented with bothobject-oriented and non-object-oriented programming systems.

Subwaveform Detection of the P, Q, R, S, T, U, and J Waveforms

As described above, electrical signals flow through all the differentmuscle tissues of the heart. For the last 100 years, conventional ECGdevices have been able to detect some of these signals in the form ofthe P, Q, R, S, T, U, and J waveforms. These waveforms have aided in thediagnosis and treatment of many heart problems.

Unfortunately, however, the P, Q, R, S, T, U, and J waveforms do notprovide a complete picture of the operation of all the different muscletissues of the heart. As a result, improved systems and methods areneeded to detect and analyze more information from the electricalsignals that flow through all the different muscle tissues of the heartas it is beating. This additional information can be used to diagnoseand treat many more heart problems.

In various embodiments, additional information is obtained from theelectrical signals produced by a heart through signal processing. Morespecifically, signal processing is added to an ECG device in order todetect more information from the electrical signals that flow throughall the different muscle tissues of the heart as it is beating.

FIG. 7 is a block diagram 700 of a conventional ECG device. Theconventional ECG device includes two or more leads or electrodes 710.Electrodes 710 are typically attached to the skin of a patient.Electrical signals produced by a beating heart are detected betweenpairs of electrodes 710. Because the heart is three-dimensional,electrodes are attached at different locations on a body to detectsignals at different corresponding locations or angles from the heart.In other words, the electrodes are placed on a body to partiallysurround the heart. One typical type of ECG includes 12 electrodes, forexample.

A voltage signal is detected between two electrodes 710 by detector 720.Detector 720 also typically amplifies the voltage signal. Detector 720can also convert the voltage signal to a digital voltage signal using ananalog to digital converter (A/D).

Detector 720 provides the detected and amplified voltage signal fromeach pair of electrodes 710 to display 730. Display 730 can be anelectronic display device including, but not limited to, a cathode raytube (CRT) device, light emitting diode (LED) device, or Liquid crystaldisplay (LCD) device. Display 730 can also be a printer device.Additionally, display 730 can include a memory device to record detectedsignals. The memory device can be, but is not limited to, a volatileelectronic memory, such as random access memory (RAM), a non-volatileelectronic memory, such as electrically erasable programmable read-onlymemory (EEPROM or Flash memory), or a magnetic hard drive.

Display 730 displays a continuous loop of the detected P, Q, R, S, T, U,and J waveforms as shown in FIG. 2 for each pair of electrodes 710.Modern ECG devices can also include a processor (not shown), such as theprocessor shown in FIG. 1, to analyze the P, Q, R, S, T, U, and Jwaveforms. For example, the processor can calculate the time periods ofthe P, Q, R, S, T, U, and J waveforms and the times between the P, Q, R,S, T, U, and J waveforms. The processor can also compare this timinginformation to stored normal information. Based on the comparison, theprocessor can determine differences from the normal data. Allinformation calculated by the processor can also be displayed on display730.

FIG. 8 is a block diagram 800 of an ECG device for detecting moreinformation from the electrical signals that flow through all thedifferent muscle tissues of the heart as it is beating, in accordancewith various embodiments. Electrodes 810 are attached to the skin of apatient, for example. Electrical signals produced by a beating heart aredetected between pairs of electrodes 810.

A voltage signal is detected between two electrodes 810 by detector 820.Detector 820 also amplifies the voltage signal. Detector 820 alsoconverts the voltage signal to a digital voltage signal using an analogto digital converter (A/D).

Detector 820 provides the detected and amplified voltage signal fromeach pair of electrodes 810 to signal processor 830. Detector 820 canalso provide the detected and amplified voltage signal from each pair ofelectrodes 810 directly to display device 840 to display theconventional P, Q, R, S, T, U, and J waveforms.

Signal processor 830 detects or calculates one or more subwaveformswithin and/or in the interval between the P, Q, R, S, T, U, and Jwaveforms of each detected and amplified voltage signal. A waveform is ashape or form of a signal. A subwaveform is shape or form of a signalthat is within or part of another signal.

Signal processor 830 can be a separate electronic device that caninclude, but is not limited to, an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA), or ageneral-purpose processor. Signal processor 830 can be softwareimplemented on another processor of the ECG device, such as a processorof display device 840. Signal processor 830 can also be a remote serverthat receives the detected and amplified voltage signal from detector820, detects or calculates one or more subwaveforms within and/or in theinterval between the P, Q, R, S, T, U, and J waveforms, and sends thedetected and amplified voltage signal and the one or more subwaveformsto display device 840.

Signal processor 830 sends one or more subwaveforms of each detected andamplified voltage signal to display device 840. Signal processor 830 canalso calculate and send to the display device 840 the time periods ofthe one or more subwaveforms, the times between the one or moresubwaveforms, and the times of the one or more subwaveforms in relationto the P, Q, R, S, T, U, and J waveforms and or the intervals betweenthe P, Q, R, S, T, U, and J waveforms. Signal processor 830 can alsocompare this timing information to stored normal timing information.Based on the comparison, signal processor 830 can determine differencesfrom the normal data and send this difference information and any of thetiming information to display device 840.

Display device 840 displays a continuous loop of the one or moresubwaveforms for each pair of electrodes 810. Display device 840 canalso display part or all of the conventional P, Q, R, S, T, U, and Jwaveforms for comparison with the one or more subwaveforms. Like display730 of FIG. 7, display device 840 of FIG. 8 can be an electronic displaydevice, a printer, or any combination of the two.

In various embodiments, an ECG device using signal processing to detectone or more subwaveforms within the P, Q, R, S, T, U, and J waveformsand/or within the intervals between the P, Q, R, S, T, U, and Jwaveforms is herein referred to as a saah ECG device. The voltagedifference signals produced by a saah ECG device are referred to as saahECG waveforms. The term “saah” is an acronym for some of theanatomically distinct portions of muscle tissue that producesubwaveforms. Specifically, saah stands for sinoatrial node (SAN), atria(right atrium (RA) and left atrium (LA)), atrioventricular node (AVN),and bundle of His (HIS). However, a saah ECG waveform is not limited toincluding subwaveforms representing the SAN, the atria, the AVN, and theHIS. A saah ECG waveform can include any subwaveform the P, Q, R, S, T,U, and J waveforms and/or within the intervals between the P, Q, R, S,T, U, and J waveforms.

FIG. 9 is an exemplary plot 900 of a saah ECG waveform of a heartbeatfrom a saah ECG device showing subwaveforms found within the P, Q, R, S,T, U, and J waveforms and/or within the intervals between the P, Q, R,S, T, U, and J waveforms, in accordance with various embodiments. Forexample, five subwaveforms 910-950 of FIG. 9 are detected within the Pwaveform and the PR segment. The time period that includes the Pwaveform and the PR segment is also called the PR interval. Subwaveform910 represents the depolarization of the SAN. Subwaveform 920 representsthe depolarization of RA and LA. Subwaveform 930 represents thedepolarization of the AVN. Subwaveform 940 represents the depolarizationHIS. Finally, subwaveform 950 represents the depolarization of thebundle branches (BB).

In various embodiments, the subwaveforms of a saah ECG waveform aredetected using signal processing. Electrodes 810 of the saah ECG of FIG.8, for example, receive electrical impulses from anatomically distinctportions of muscle tissue or cells. The electrical impulses ofanatomically distinct portions of muscle tissue of the heart havedistinct frequencies. Through animal and human experimentation, thedistinct frequency, frequency range, or frequency band of theanatomically distinct portions of muscle tissue of the heart are found.These distinct frequency bands of anatomically distinct portions ofmuscle tissue of the heart provide predetermined data or information forsignal processing. In other words, the band pass frequency filtering ofthe signal processing is determined from the experimental datacollected. A saah ECG device then employs one or more frequency bandpass filters to detect the one or more subwaveforms.

FIG. 10 is an exemplary block diagram 1000 showing a signal processingalgorithm for detecting five subwaveforms within the PR interval of aconventional ECG waveform, in accordance with various embodiments.Sampling block 1010 samples the electrical impulses in the PR intervaltime period of each heart. This is shown graphically in FIG. 1000 byseparating PR interval 1020 from ECG waveform 200. The electricalimpulses in the PR interval time period are sampled using electrodes 810and detector 820 of FIG. 8, for example. Detector 820 of FIG. 8 can alsoamplify and convert the analog signal into a digital signal for digitalprocessing.

The signal processing can be performed directly on the time domainsignal received from a detector or the time domain signal received froma detector can be converted to the frequency domain for algorithmicprocessing. In FIG. 10, block 1030 converts the PR interval time domainsignal to a PR interval frequency domain signal. The time domain signalis converted into a frequency domain signal using a Fourier transform,for example.

As described above, through animal and/or human experimentation, thefrequency bands associated with depolarization of the SAN, atria, AVN,HIS, and BB of the heart are determined. Based on these frequency bands,band pass filters are created. Blocks 1041-1045 represent the band passfilters created to filter the PR interval frequency domain signal forfrequency bands of the SAN, atria, AVN, HIS, and BB of the heart,respectively.

In block 1050, the frequency domain subwaveforms detected from the bandpass filtering the frequency bands of the SAN, atria, AVN, HIS, and BBof the heart are summed. In block 1060, the filtered and summedfrequency domain signal of the PR interval is converted back to a timedomain signal. The frequency domain signal is converted into a timedomain signal using a Fourier transform, for example.

The PR interval filtered and summed time domain signal 1070 includesfive time domain subwaveforms 910-950. Subwaveforms 910-950 representdepolarization of the SAN, atria, AVN, HIS, and BB of the heart,respectively. Time domain signal 1070 can be used to replace PR interval1020 in ECG waveform 200, for example. As a result, a saah ECG waveformis produced.

FIG. 10 shows a signal processing algorithm for detecting fivesubwaveforms. However, similar steps can be applied to detect fewer thanfive waveforms or more than five waveforms. Also, the steps of FIG. 10describe detecting subwaveforms within the PR interval. However, similarsteps can be applied to detect subwaveforms within the P, Q, R, S, T, U,and J waveforms and/or within one or more of the intervals between theP, Q, R, S, T, U, and J waveforms. In addition, the steps of FIG. 10describe converting time signals to the frequency domain and then backto the time domain. One of ordinary skill in the art can appreciate thatband pass filters can be applied directly to the time domain signal toprovide the same result.

FIG. 11 is an exemplary block diagram 1100 of a saah ECG device thatdisplays conventional ECG waveforms, saah ECG waveforms, and saah ECGdata, in accordance with various embodiments. In block 1110, patientheart signals are obtained. These heart signals can be obtained throughnoninvasive electrodes placed on the skin, such as electrodes 810 shownin FIG. 8. In various embodiments, heart signals may also be obtainedusing invasive electrodes placed directly on the heart. In block 1120,the heart signals are detected using a detector and amplified.

In block 1130, the detected and amplified heart signals are processedusing a signal processor. The signal processor detects the conventionalP, Q, R, S, T, U, and J waveforms and sends them to the display of block1160. The signal processor also detects or calculates subwaveformswithin the conventional P, Q, R, S, T, U, and J waveforms and/or withinintervals between the conventional P, Q, R, S, T, U, and J waveforms.The signal processor sends the subwaveforms to block 1140 for furtherprocessing. The processor of block 1140 produces the saah ECG waveformthat includes the subwaveforms and sends the saah ECG waveform to thedisplay of block 1160. The processor of block 1140 calculates additionalinformation or new data from the saah ECG waveform. This new data caninclude, but is not limited to, timing information about thesubwaveforms, timing information about the intervals between thesubwaveforms, and timing information about the subwaveforms and theirrelation to the conventional P, Q, R, S, T, U, and J waveforms. In block1150, this new data is sent to the display of block 1160.

The display of block 1160 displays a continuous loop of the conventionalECG waveform, the saah ECG waveform, and the new data from thesubwaveforms. The display of block 1160 can display this information onan electronic display or print it on paper. The display of block 1160can also record this information. The display of block 1160 can recordthis information on any type of memory device.

FIG. 12 is an exemplary plot 1200 of the information displayed by thesaah ECG device of FIG. 11, in accordance with various embodiments. Plot1200 includes conventional ECG waveform 1210 and saah ECG waveform 1220.Saah ECG waveform 1220, for example, includes, among others, fivesubwaveforms A-E representing the depolarization of the SAN, the RA andLA, the AVN, the HIS, and the BB, respectively.

Plot 1200 also shows new data or timing information about thesubwaveforms and their relation to the conventional P, Q, R, S, T, U,and J waveforms. For example, the time interval between line 1231 andline 1232 relates subwaveform A of saah ECG waveform 1220 to P waveform1240 of conventional ECG waveform 1210. The time interval between line1232 and line 1233 relates subwaveforms B and C of saah ECG waveform1220 to P waveform 1240 conventional ECG waveform 1210. The timeinterval between line 1233 and line 1234 relates subwaveforms D and E ofsaah ECG waveform 1220 to PR segment 1250 conventional ECG waveform1210.

FIG. 13 is an exemplary block diagram 1300 of a saah ECG device thatdisplays conventional ECG waveforms, saah ECG waveforms, saah ECG data,and saah ECG automatic pattern recognition diagnosis information, inaccordance with various embodiments. In block 1310, patient heartsignals are obtained. These heart signals can be obtained throughnoninvasive electrodes placed on the skin, such as electrodes 810 shownin FIG. 8. In various embodiments, heart signals may also be obtainedusing invasive electrodes placed directly on the heart. In block 1320,the heart signals are sampled or detected using a detector. The heartsignals may also be amplified.

In block 1330, the sampled heart signals are processed using a signalprocessor. The signal processor produces four different types ofinformation from the sampled heart signals. As shown in block 1340, thesignal processor produces conventional ECG waveforms including theconventional P, Q, R, S, T, U, and J waveforms and sends them to display1380. As shown in block 1350, the signal processor produces saah ECGwaveforms. These saah ECG waveforms include subwaveforms of theconventional P, Q, R, S, T, U, and J waveforms and the intervals betweenthem. Note that the arrow between blocks 1330 and 1350 show informationfollowing in both directions. This shows that information from the saahECG waveforms is further analyzed by the signal processor.

As shown in block 1360, the signal processor further analyzes the saahECG waveforms to produce saah ECG data. This saah ECG data is sent todisplay 1380. Additionally, as shown in block 1370, the signal processorfurther analyzes the saah to obtain endocardium and epicardium data.This data is compared to recorded normal and abnormal data. The signalprocessor then produces automatic pattern recognition diagnosis (APD)information, and this information is sent to display 1380. APDinformation is, for example, patterns and/or colors that allow a user toeasily and quickly determine that normal or abnormal endocardium and/orepicardium data was found.

Systems and methods for detecting ECG subwaveforms are described in the'204 Patent, which is incorporated by reference in its entirety.

System for Detecting ECG Subwaveforms

In various embodiments, an electrocardiography (ECG) system fordetecting one or more subwaveforms within the P, Q, R, S, T, U, and Jwaveforms or in an interval between the P, Q, R, S, T, U, and Jwaveforms is provided. Returning to FIG. 8, the ECG system includes twoor more electrodes 810, a detector 820, a signal processor 830, and adisplay device 840.

Two or more electrodes 810 are placed near a beating heart and receiveelectrical impulses from the beating heart. Two or more electrodes 810are shown in FIG. 8 as noninvasive electrodes that are attached to theskin of a patient. In various embodiments, two or more electrodes 810can be invasive electrodes placed directly on or within heart tissue.

Detector 820 is electrically connected to two or more electrodes 810.Detector 820 detects the electrical impulses from at least one pair ofelectrodes of the two or more electrodes 810. Detector 820 converts theelectrical impulses to an ECG waveform for each heartbeat of the beatingheart. Detector 820, for example, samples the electrical impulses. Invarious embodiments, detector 820 further amplifies the ECG waveform. Invarious embodiments, detector 820 further performs analog to digital(A/D) conversion on the ECG waveform. In various embodiments, detector820 provides an ECG waveform with a higher signal-to-noise (S/N) ratiothan conventional ECG devices.

Signal processor 830 is electrically connected to detector 820. Signalprocessor 830 receives the ECG waveform from detector 820. Signalprocessor 830 detects or calculates one or more subwaveforms within P,Q, R, S, T, U, and J waveforms of the ECG waveform or in an intervalbetween the P, Q, R, S, T, U, and J waveforms that represent thedepolarization or repolarization of anatomically distinct portions ofmuscle tissue of the beating heart. Signal processor 830 produces aprocessed ECG waveform that includes the one or more subwaveforms foreach heartbeat.

Signal processor 830 can be a separate device, can be software runningon a device of detector 820 or display device 840, or can be softwarerunning on a remote server and communicating with detector 820 anddisplay device 840 through one or more communication devices. Signalprocessor 830 can be a separate device that includes, but is not limitedto, an application specific integrated circuit (ASIC) or a fieldprogrammable gate array (FPGA) or a general-purpose processor. A generalpurpose processor can include, but is not limited to, a microprocessor,a micro-controller, or a computer such as the system shown in FIG. 1.Signal processor 830 can be software implemented on another processor ofthe ECG device, such as a processor of display device 840. Signalprocessor 830 can also be a remote server that receives the detected andamplified difference voltage signal from detector 820, detects orcalculates one or more subwaveforms within and/or in the intervalbetween the P, Q, R, S, T, U, and J waveforms, and sends the detectedand amplified different voltage signal and the one or more subwaveformsto display device 840.

Display device 840 receives the processed ECG waveform for eachheartbeat and displays the processed ECG waveform for each heartbeat.The processed ECG waveform is called a saah ECG waveform, for example.As described above, display device 840 can be an electronic displaydevice including, but not limited to, a cathode ray tube (CRT) device,light emitting diode (LED) device, or Liquid crystal display (LCD)device. Display device 840 can also be a printer device or anycombination of an electronic display device and a printer. Additionally,display device 840 can include a memory device to record saah ECGwaveforms, saah ECG data and conventional ECG waveforms and data. Thememory device can be, but is not limited to, a volatile electronicmemory, such as random access memory (RAM), a non-volatile electronicmemory, such as electrically erasable programmable read-only memory(EEPROM or Flash memory), or a magnetic hard drive.

In various embodiments, the detected one or more subwaveforms include atleast one subwaveform representing depolarization of the sinoatrial node(SAN), the atria (right atrium (RA) and left atrium (LA)), theatrioventricular node (AVN), the bundle of His (HIS), or the bundlebranches (BB) of the beating heart.

In various embodiments, the display device 840 further displays the ECGwaveform for comparison with the processed ECG waveform.

In various embodiments, signal processor 830 further calculates timinginformation about the one or more subwaveforms, timing information aboutthe intervals between the one or more subwaveforms, and timinginformation about the one or more subwaveforms and their relation to theP, Q, R, S, T, U, and J waveforms of the ECG waveform for eachheartbeat. Display device 840 further receives this timing informationfrom signal processor 830. Display device 840 displays the timinginformation about the one or more subwaveforms, the timing informationabout the intervals between the one or more subwaveforms, and the timinginformation about the one or more subwaveforms and their relation to theP, Q, R, S, T, U, and J waveforms of the ECG waveform for eachheartbeat.

In various embodiments, the ECG system further includes a memory device(not shown). The memory device receives the ECG waveform and theprocessed ECG waveform from the signal processor.

In various embodiments, the memory device further includes normallyprocessed ECG waveform data. Normally processed ECG waveform data isstored on the memory device using signal processor 830 or ageneral-purpose processor (not shown). Signal processor 830 furthercompares the processed ECG waveform to the normally processed ECGwaveform data and calculates a status condition based on the comparison.The status conditions are, for example, normal, suspicious, or abnormal.

In various embodiments, the ECG system includes a second display device(not shown) surrounding a rotating button (not shown). Signal processor830 further sends a colored pattern to the second display device basedon the status condition. The second display device provides automaticpattern recognition diagnosis (APD).

Method for Detecting ECG Subwaveforms

FIG. 16 is a flowchart showing a method 1600 for detecting subwaveformswithin the P, Q, R, S, T, U, and J waveforms of an ECG waveform of aheartbeat or in an interval between the P, Q, R, S, T, U, and Jwaveforms, in accordance with various embodiments.

In step 1610 of method 1600, electrical impulses are detected between atleast one pair of electrodes of two or more electrodes placed proximateto a beating heart using a detector. The electrical impulses areconverted to an ECG waveform for each heartbeat of the beating heartusing the detector.

In step 1620, the ECG waveform for each heartbeat is received from thedetector using a signal processor. One or more subwaveforms within P, Q,R, S, T, U, and J waveforms of the ECG waveform for each heartbeat or inan interval between the P, Q, R, S, T, U, and J waveforms that representthe depolarization or repolarization of an anatomically distinct portionof muscle tissue of the beating heart are detected using the signalprocessor. A processed ECG waveform that includes the one or moresubwaveforms for each heartbeat is produced using the signal processor.

In step 1630, the processed ECG waveform is received from the signalprocessor and the processed ECG waveform is displayed using a displaydevice.

Computer Program Product for Detecting ECG Subwaveforms

In various embodiments, computer program products include a tangiblecomputer-readable storage medium whose contents include a program withinstructions being executed on a processor so as to perform a method fordetecting subwaveforms within the P, Q, R, S, T, U, and J waveforms ofan ECG waveform of a heartbeat or in an interval between the P, Q, R, S,T, U, and J waveforms. This method is performed by a system thatincludes one or more distinct software modules.

FIG. 17 is a schematic diagram of a system 1700 that includes one ormore distinct software modules that perform a method for detectingsubwaveforms within the P, Q, R, S, T, U, and J waveforms of an ECGwaveform of a heartbeat or in an interval between the P, Q, R, S, T, U,and J waveforms, in accordance with various embodiments. System 1700includes detection module 1710, processing module 1720, and displaymodule 1730.

Detection module 1710 detects electrical impulses between at least onepair of electrodes of two or more electrodes placed proximate to abeating heart. Detection module 1710 converts the electrical impulses toan ECG waveform for each heartbeat of the beating heart.

Processing module 1720 receives the ECG waveform for each heartbeat.Processing module 1720 detects one or more subwaveforms within P, Q, R,S, T, U, and J waveforms of the ECG waveform for each heartbeat or in aninterval between the P, Q, R, S, T, U, and J waveforms that representthe depolarization or repolarization of an anatomically distinct portionof muscle tissue of the beating heart. Processing module 1720 produces aprocessed ECG waveform that includes the one or more subwaveforms foreach heartbeat.

Display module 1730 receives the processed ECG waveform. Display module1730 displays the processed ECG waveform.

Automated ECG Analysis and Diagnosis

As described above, to date the accuracy rate of automated ECG analysisand diagnosis systems has been a problem in clinical applications. Thereare at least three technical reasons for this. 1. The conventional ECGwaveform is morphological and generally no consistent mapping points canbe found. 2. Conventional ECG measurements have not provided informationspecific different parts of the heart muscle. 3. Automated ECG waveformanalysis has generally resulted in a high number of false positives forboth normal and abnormal populations. However, ECG remains one of themost extensively used clinical tools, despite the lack of accuratesystems for automated ECG analysis and diagnosis. As a result, there isa significant need for such systems. Recent advancements have addressedthe conventional ECG waveform measurement problem. Specifically, thesystems of the '204 Patent and the '930 Patent have allowed thedifferent frequency domain signals from different parts of the heartmuscle to be measured.

Additional systems, however, are needed to further address the technicalproblems of analyzing the shape and form of these frequency domainsignals and distinguishing disease conditions from false positives innormal and abnormal populations.

Conventional ECG Waveform Analysis Problems

Analysis of conventional ECG waveforms for clinical diagnosis has beenlimited by a number of problems for more than a century. (1) It has beendifficult to correctly confirm the start point of the P wave. The reasonfor this is that the start point of the P wave is on “a parallelequipotential line,” which needs to be determined. This hastraditionally been determined through guessing. If the starting point ofeach heartbeat cannot be correctly identified, then all subsequentparameter measurements will be wrong. The normal value for theconduction time from the SA node (the starting point of the P wave) tothe atrium is only around 30 ms. As a result, a small mistake in thelocation of the starting point can make a big difference in themeasurement of this conduction time.

FIG. 14 is an exemplary plot 1400 of a conventional ECG waveform showinghow a small error in the starting point of the P wave can cause a largeerror in all subsequent time measurements, in accordance with variousembodiments. The starting point of P wave 1430 of conventional ECGwaveform 1410 is somewhere on parallel equipotential line 1420. Eachsquare of the grid of plot 1400 represents a time of 40 milliseconds(ms). Parallel equipotential line 1420 spans about one square of thegrid of plot 1400. As a result, picking four different closely spacedpoints along parallel equipotential line 1420 produces starting pointtimes that vary among 8 ms, 16 ms, 24 ms, and 32 ms within the onesquare of the grid of plot 1400. In other words, small differences inthe selection of the starting point of P wave 1430 can mean largedifferences in the timing values used for P wave 1430. It can alsoaffect all of the other components of ECG waveform 1410. This is becausethe starting point of P wave 1430 is also the starting point of theentire ECG waveform 1410.

(2) At the PR interval, it has been difficult to identify the specificPA, AH, or HV intervals within PR interval. When the PR interval isabnormal, in particular, it can only be estimated and cannot bemeasured. Also, often the end of the PR interval cannot be confirmed, asthere is no equipotential and the starting point of QRS wave appears tobe an upward arc angle.

FIG. 15 is an exemplary plot 1500 of a conventional ECG waveform showinghow the end of the PR interval cannot be confirmed due to an upward arcangle of the starting point of QRS wave, in accordance with variousembodiments. The timing measurement of PR interval 1520 of conventionalECG waveform 1510 is a very important measurement since it the onlymeasurement for the atrium. As described above, it is difficult tomeasure the starting point of the P wave, which is also the start of PRinterval 1520. It turns out it is just as difficult if not moredifficult to measure the ending point of PR interval 1520. This is dueto changes to parallel equipotential line 1530 at the ending point of PRinterval 1520 as shown in plot 1500. Parallel equipotential line 1530 isnot parallel at all but rather is shaped like an upward arc angle.Therefore, it is very difficult to accurately map PR interval 1520. Thestandard value for PR interval 1520 is 120-200 ms, for example. Incontrast, PR segment 220 of ideal conventional ECG waveform of FIG. 2has a parallel equipotential line just before a downward arc angle tothe QRS wave.

(3) It has been difficult to identify a difference between the STsegment of a normal person and the ST segment of an abnormal person. Inother words, the ST segment appears to be exactly abnormal for normalpeople and exactly normal for abnormal people. Also, and the J pointoften disappears, making it impossible to determine. As a result, thestandards for the ST segment often cannot be applied.

In summary, at an abnormal moment, signals of a conventional ECGwaveform often shift positions, and the waveform is changed to adifferent shape, making it difficult or impossible to estimate. If aconventional ECG waveform is changed in such a way and humanintelligence or experience is still relied on to diagnose, a largeamount of accuracy is lost.

Automated ECG Analysis and Diagnosis Using AI

As described above, additional systems are needed to further address thetechnical problems of analyzing the shape and form of the frequencydomain signals of a conventional ECG waveform and distinguishing diseaseconditions from false positives in normal and abnormal populations usingthe conventional ECG waveform.

In various embodiments, these technical problems are addressed by 1.applying artificial intelligence (AI) algorithms to characterize theshape and form of the frequency domain signals of a conventional ECGwaveform; 2. comparing the characterized shape and form of the frequencydomain signals to a database of characterized signals from normal andabnormal populations using human like AI algorithms and non-human likeAI algorithms; and 3. annotating the conventional ECG waveform withdiagnosis information based on the comparison.

FIG. 18 is an exemplary block diagram 1800 of a system for automated ECGanalysis and diagnosis using AI, in accordance with various embodiments.In step 1801 of the system of FIG. 18, two or more electrodes areattached to the skin of a patient to obtain electrical signals from theheart muscle. In various alternative embodiments, the two or moreelectrodes may be attached directly and invasively to the heart muscle.The two or more electrodes are, for example, conventional ECG leads.

In step 1802, electrical heart signals are obtained from the two or moreelectrodes.

In step 1803, the electrical heart signals are detected and amplified.

In step 1804, the amplified signals are processed. For example, thesignals from a number of different conventional ECG leads are combined.

In step 1805, the combined signals form a conventional ECG waveform.

In step 1806, the conventional ECG waveform is displayed or printed, forexample.

In step 1810, the detected signals of step 1803 are obtained andprocessed to produce two or more frequency domain signals.

In step 1811, the two or more frequency domain signals are processed forcharacteristics of cardiac electrophysiological signals using one ormore AI algorithms.

In step 1812, the cardiac electrophysiological characteristics of thetwo or more frequency domain signals are compared to databases ofsimilar cardiac electrophysiological characteristics for normal andabnormal populations using the system of FIG. 18.

In step 1820, the cardiac electrophysiological characteristics of thetwo or more frequency domain signals are compared to the databases usinghuman like AI algorithms. These human like AI algorithms can include,but are not limited to, an expert and cardiologist system 1821, an ECGdiagnosis system 1822, an intelligent signals unsupervised featurelearning system 1823, a general ECG problem solver 1824, and a semanticECG waveform system 1825.

In step 1830, the cardiac electrophysiological characteristics of thetwo or more frequency domain signals are compared to the databases usingnon-human like AI algorithms. These human like AI algorithms caninclude, but are not limited to, a neural network algorithm 1831 and adeep learning algorithm 1832. The neural network algorithm 1831 caninclude a shallow network 1833. The deep learning algorithm 1832 caninclude a deep network 1834. This deep network 1834 can include, but isnot limited to, a convolution all neural network (CNN) 1835, a deepbelief net (DBN) 1836, or a restricted Boltzmann machine (RBM).

In step 1840, the results from steps 1820 and 1830 are combined toprovide diagnosis information for the conventional ECG waveform.

In step 1806, this diagnosis information is displayed on theconventional ECG waveform.

The system of FIG. 18 provides a number of advantages over conventionalautomated analysis and diagnosis systems. First of all, it reducesmedical and insurance expenses. As a result of the automated diagnosisinformation patients can avoid invasive and expensive examinations.Secondly, the quick and accurate diagnosis information allows prompt andaccurate treatment. In other words, the shortened time for diagnosisallows treatment to occur without delay. Thirdly, the quick and accuratediagnosis helps train doctors more efficiently and can significantlyreduce misdiagnosis rates. Fourthly, the quick and accurate diagnosisinformation can help in the research and development of new target drugsfor cardiac treatments. Finally, the use of these AI algorithms in ECGmakes these instruments intelligent systems.

In various embodiments, the diagnosis information presented in step 1806can include, but is not limited to, diagnosis markers or more accuratetiming information.

Analysis and Diagnosis System

The systems of the '204 Patent and the '930 Patent have used differentsignal processing methods to detect the harmonic signals anddiscontinuity points of a conventional ECG waveform. In variousembodiments, artificial intelligence (AI) in conjunction with a databaseof normal and abnormal ECG data is used to detect the harmonic signalsand discontinuity points of a conventional ECG waveform and to annotatecardiac electrophysiological signals in the ECG waveform as normal orabnormal.

FIG. 19 is a block diagram 1900 of an ECG system for identifying andannotating cardiac electrophysiological signals in an ECG waveform asnormal or abnormal during measurement of the ECG waveform, in accordancewith various embodiments. Electrodes 1910 are attached to the skin of apatient in a noninvasive measurement, for example. In an alternativeembodiment, electrodes 1910 are attached directly on the surface of abeating heart of a patient. Electrical signals produced by a beatingheart are detected between pairs of electrodes 1910.

A voltage signal is detected between two electrodes 1910 by detector1920. Detector 1920 also amplifies the voltage signal. Detector 1920converts the electrical impulses to an ECG waveform for each heartbeatof the beating heart. Detector 1920 converts the voltage signal to adigital voltage signal using an analog to digital converter (A/D), forexample. Detector 1920 provides the detected and amplified voltagesignal from each pair of electrodes 1910 directly to display device 1940to display the ECG waveform. The ECG waveform includes conventional P,Q, R, S, T, U, and J waveforms, for example. Detector 1920 also providesthe detected and amplified voltage signal from each pair of electrodes1910 directly to processor 1930.

Processor 1930 can be a separate electronic device that can include, butis not limited to, an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), or a general-purpose processor orcomputer, such as the system of FIG. 1. Processor 1930 can be softwareimplemented on another processor of the ECG device, such as a processorof display device 1940. Processor 1930 can also include a remote servercomputer.

Processor 1930 receives the ECG waveform for at least one heartbeat fromdetector 1920. Processor 1930 converts the ECG waveform to a frequencydomain waveform. Processor 1930 separates the frequency domain waveforminto two or more different frequency domain waveforms. Processor 1930converts the two or more different frequency domain waveforms into aplurality of time domain cardiac electrophysiological subwaveforms anddiscontinuity points between these subwaveforms of the ECG waveform.

Processor 1930 compares the plurality of subwaveforms and discontinuitypoints to a database (not shown) of subwaveforms and discontinuitypoints for cardiac electrophysiological signals of ECG waveforms for aplurality of known and normal and abnormal patients. Processor 1930identifies at least one subwaveform or one or more discontinuity pointsof the plurality of subwaveforms and discontinuity points as a normal orabnormal electrophysiological signal of the ECG waveform based on thecomparison.

Display device 1940 is an electronic display device, a printer, or anycombination of the two. Display device 1940 displays the ECG waveformfor the at least one heartbeat of the beating heart. Display device 1940also displays one or more markers at the location of the at least onesubwaveform or the one or more discontinuity points on the ECG waveformand identifies the one or more markers as a normal or abnormal cardiacelectrophysiological signal. For example, FIGS. 35 and 36 show how oneor more markers are displayed on ECG waveforms to indicate normal orabnormal cardiac electrophysiological signals. The one or more markerscan be identified as a normal or abnormal cardiac electrophysiologicalsignal using symbols, colors, or text, for example.

In various embodiments, processor 1930 converts the ECG waveform to afrequency domain waveform, separates the frequency domain waveform intotwo or more different frequency domain waveforms, and converts the twoor more different frequency domain waveforms into a plurality of timedomain cardiac electrophysiological subwaveforms and discontinuitypoints using an artificial intelligence algorithm. The artificialintelligence algorithm includes a multivariable calculus algorithm, forexample.

In various embodiments, processor 1930 compares the plurality ofsubwaveforms and discontinuity points to a database of subwaveforms anddiscontinuity points using a human like artificial intelligencealgorithm.

In various embodiments, the human like artificial intelligence algorithmincludes an expert and cardiologist system. This system evaluates thecomparison based on morphological rules developed from cardiologists. Inother words, the shape differences are compared using rules based onpattern recognition and doctors' experiences.

In various embodiments, the human like artificial intelligence algorithmincludes an ECG diagnosis system. This system evaluates the comparisonbased on morphological patterns and their correlation to specificdiseases.

In various embodiments, the human like artificial intelligence algorithmincludes an intelligent signals unsupervised feature learning system.This system evaluates the comparison based on morphological patternslearned over time by the system.

In various embodiments, the human like artificial intelligence algorithmincludes a general ECG problem solver. This system evaluates thecomparison based on one or more known conditions or conflictingconditions including, but not limited to, heart failure (HF), atriumfibrillation (AF), atrial conductor block, premature atrial contraction(PAC), premature ventricular contraction (PVC), atrial tachycardia, andventricular tachycardia.

In various embodiments, the human like artificial intelligence algorithmincludes a semantic ECG waveform system. This system evaluates thecomparison based on additional signal processing rather than patternrecognition.

In various embodiments, processor 1930 compares the plurality ofsubwaveforms and discontinuity points to a database of subwaveforms anddiscontinuity points using a non-human like artificial intelligencealgorithm.

In various embodiments, the human like artificial intelligence algorithmincludes a neural network. The neural network can be a shallow network,for example.

In various embodiments, the human like artificial intelligence algorithmincludes a deep learning algorithm. The deep learning algorithm caninclude a deep network, for example. The deep network can include, butis not limited to, convolution all neural network (CNN), a deep beliefnet (DBN), or a restricted Boltzmann machine (RBM).

Vital Sign Monitoring

As described above, the ECG waveform is the most important parameter ofVital Sign Monitoring. Death is mainly caused by cardiac changes withinthe heart, even when there is no disease. The cardiac informationchanges from physiological to pathological. However, the current VitalSigns Monitor is only a warning device after the occurrence of criticalevents.

As a result, there is a need for Vital Sign Monitoring ECG systems andmethods that provide automatic detection of possible disease and nolonger simply act as an alarm device after the occurrence of criticalevents.

In various embodiments, a Vital Sign Monitoring ECG system correctlydisplays the signals that the traditional ECG cannot. It has keyanatomical signals, including invasive parameters using noninvasivemonitoring and fatal cardiac signals if/when abnormal events occur. Itis able to scan, record, extract, analyze, and diagnose informationlacking in traditional Vital Sign Monitoring.

In various embodiments, a Vital Sign Monitoring ECG system includes aventricular monitoring module, capable of reading myocardial infarctionwithout ST-T changes, acute coronary syndrome, acute myocardialischemia, etc. This next-generation monitoring has three displays: a newECG waveform, an artificial intelligence-based diagnostic colorseparation waveform, and quantitative data.

In various embodiments, a Vital Sign Monitoring ECG system does notsolely rely on waveforms, contains automatic navigation, mapping ofcardiac markers and diagnostic tools. This artificial intelligence (AI)system does not require a large database (because no two hearts aresimilar; each heartbeat is different and abnormal data varies evenmore). For example, the system can include a small database or rulesderived from a database. The system does not take significant amounts oftime. For example, 3-5 seconds of testing results in an automaticdisplay.

In various embodiments, a Vital Sign Monitoring ECG system includesquantitative data for the new ECG waveform. Digital display is the mostbasic and critical requirement of a monitor. Traditional ECG's deviceshave many standardized data values, but they cannot be used because theycannot be measured precisely and are not automatically measured. Othermonitoring devices use digitized data, such as Blood Pressure, SpO2,Heart Rate, Respiration Rate, Pulse Rate, and Temperature in order toachieve fast, accurate real-time information.

In various embodiments, a Vital Sign Monitoring ECG system includes adisplay that is a combination of cardiac electrophysiology andhemo-dynamics. The left and right ventricle are expressed in color; thedisplay itself is expressed as a specified “form”. This can be readeasily, without training, and diagnosed within one second. This displayreads faster than traditional data parameters, where after the monitordisplays the data values, the doctor must then analyze and think aboutnormal ranges.

In various embodiments, a Vital Sign Monitoring ECG system includes ananti-drift filter. In long-time monitoring, heart signal drift is themost common, major problem. Currently the best anti-drift filter isprovided by the German company Philips. Their “linear phase filter andHigh-pass filter,” however, removes the DC signal at the same time theleft and right low-frequency signal is also removed. The resultant phaseis seriously distorted because the low-frequency signal is removed atthe same time; the lower-frequency signal is eliminated (extremelyuseful signal) and there are no mathematical changes.

The Vital Sign Monitoring ECG system includes an artificial intelligenceautomatic adjustable selection frequency segment. It understands whichband is disturbed, and which band to remove. It also includes a varietyof hybrid filters, including “linear and nonlinear phase” filters. It isvery important that there is no phase distortion. The waveform is true,and the anti-drift effect is better. Drifts out of the display range aredisplayed as a straight line, signal overlapping and other serious driftsignals are recoverable in this technology.

FIG. 20 is an exemplary diagram 2000 of a new ECG waveform and acorresponding traditional ECG waveform, in accordance with variousembodiments. The new or SAAH ECG waveform at the top of diagram 2000shows additional subwaveforms and discontinuity points. For example, thenew ECG waveform includes left and right bundle branches (BB)subwaveform 2010, bundle of HIS subwaveform 2020, point where AV nodeand bundle of HIS join 2030, AV node subwaveform 2040, point where AVnode and Atrial area join 2050, and heart beat starting point 2060.

In contrast to the new ECG waveform, the traditional ECG waveform has nosubwaveforms or wavelets in the P-R interval and the ST segment. Theapplication of the new ECG waveform in Vital Sign Monitoring is moreimportant because it can save time; every second is important in the ICUand enhance the real-world function of monitoring. The primary functionof the Vital Sign Monitoring is to detect potential danger in advance,as well as Cardiac Risk Factors. While the current monitoring is only analarm function. When the Heart Rate changes, ventricular fibrillation(VF) occurs, and/or ventricular tachycardia (VT) happens, it is too latefor medical rescue.

FIG. 21 is an exemplary diagram 2100 of an ECG diagnostic colorseparation waveform, in accordance with various embodiments. The ECGdiagnostic color separation waveform of diagram 2100 displays theanatomical parts of the heart in color within the P-R interval and ST-Tinterval, while the waveform itself is depicted as a traditional ECGwaveform. Below the ECG diagnostic color separation waveform is a colorkey linking the colors to the anatomical parts of the heart. On theright are the parameter timing values with standard ranges and the testdata. Doctors can search, review, and observe the normal time periodsneeded by patients in the monitor. When the data value exceeds thenormal range, before a heart attack, before AMI, and before ACS, thenumber will automatically prompt an alarm. The doctor can choosedifferent colors of the numbers. Current monitors only have a heart ratedigital alarm.

FIG. 22 is an exemplary diagram 2200 of a new ECG waveform and acorresponding traditional ECG waveform, showing some additionalparameter timing values, in accordance with various embodiments.

FIG. 23 is an exemplary table 2300 showing the timing parameter valuesof the ECG waveform in FIG. 22, including new timing parameter values,in accordance with various embodiments. Six timing parameter values areadded including, BB-J interval 2310, BB-ST interval 2320, i-R segment2330, R-j segment 2340, J-Tp 2350 (TP=[1^(st) beat] T wave terminalpoint to [2^(nd) beat] P wave initial point) segment, and T-T interval2360. These parameters are applied in cases where the ECG waveform isfinely changed and the judgment results are difficult to be made, suchas wide QRS wave group, J-point vanishing, ST segment arc change,non-elevation or descent of ST segment, separation of atrium andventricle, di-synchronization, degree of heart failure, and other imagechanges, which can be used to help analyze and judge the parameters.

BB-J Interval

For BB-J interval 2310, the timing is from Bundle branches to J-point.The most complex part is due to the anatomical features. The beam branchis divided into left bundle branch first and then right bundle branch.If block occurs, it will affect the shape and position of J-point. Oncethe starting point of the ST segment changes, the J-point will changewith the delay of the bundle branch. The newly displayed J-point is notactually the original position, but is affected by the equipotentialhorizontal line. The J-point varies from point to point. Sometimes, itis convoluted in the QRS wave group and buried in the RS branch to forman elevation or a descent, resulting in the broadening of the QRS wavegroup. Traditional ECG is not shown. (This parameter cannot be measuredby traditional ECG, because there are no measurement points in the BBregion (there are no waves in the beam branch), and J-point displacementcannot be displayed). This parameter can be used to analyze whether itis simply left bundle branch block or right bundle branch block, or ifit is left or right bundle branch block caused by J-point shift CAD. LCxvascular occlusion can easily cause left bundle branch block, and RCAvascular occlusion can easily cause right bundle branch block.

BB-ST Interval

For BB-ST interval 2320, the timing is from Bundle branches to STsegment. The key point of this part is the timing of the end of bundlebranches and the connection segment of Purkinje's fibers. Thetraditional ECG cannot differentiate the end point of the ST segment.Data values of the initial interval from bundle branch to ventriculardepolarization to ventricular repolarization can be analyzed. Increasedanalysis of left/right bundle branch block and CAD, heart failure, andother diseases, and analysis of primary and secondary lesions becomeavailable. In particular, when the ST segment is not elevated ordescended, it is of great application value, because the H-P system(His-Purkinje's System) plays an extremely important role in the heart'spower output, electrical excitation, and electrical conduction.

i-R Segment

For i-R segment 2330, the timing is for the path of the connectionbetween the bundle branch and the initial end of the Purkinje's fibersto the maximum potential peak of ventricular depolarization. Theanatomical location is located in the inner ventricular septum and thedepolarizing position of the apex of the heart. Many diseases can causesignal variations, morphological changes, time lengthening, timeshortening, time overlapping, or burial involving time segments,especially in the area of the P-R segment. These include patients withunrelenting Tachycardia, WPW, short PR syndrome, and so on. It is ofgreat clinical significance in differential diagnosis and confirmationof normal and abnormal images. Traditional ECG cannot distinguish theprecise position of the I-point and traditional ECG cannot accuratelyobtain i-R segment 2330 without the I-point position and its data value.

R-j Segment

This R-j segment, if the traditional ECG display is abnormal, is themost common and most difficult change to study. For R-j segment 2340,the timing is for the interval from the highest R wave peak of QRS tothe terminal end of Purkinje's fibers. The anatomic location is thejunction between the anterior wall and the apex of the free wall of theventricle and the deep endocardium of Purkinje's terminal end. The R-jsegment will be raised, lowered, lengthened, shortened, disappeared,inverted, etc. When a wide QRS wave group appears, J-point is embeddedinto RS. Meanwhile, the J-point position shown by the traditional ECG isthe starting point of the equipotential line, rather than the realJ-point. In traditional ECG, R peaks are easy to be identified, whileJ-point is not easy to be identified. R-S-J segments cannot be analyzedif they are widened. It is very important to confirm the data value ofthe R-j segment, which is imperative for distinguishing normal fromabnormal. With traditional ECG is difficult to confirm the J-point,especially when the J-point is shifted, so it cannot be obtained withoutthis parameter and data value.

J-Tp Segment

The difficulty of this parameter is in J-point positioning. Intraditional ECG, the J-point is often a >120° arc angle and obtaining itwith precision is extremely difficult. Tp is the peak of T wave, whichis easy to locate. For J-Tp segment 2350, the timing is for the STsegment before ventricular repolarization—the highest peak ofventricular repolarization. The anatomical site is the site of cardiacsystolic phase, phase II plus phase III. In this section of the new ECG,there are ST waves, which will show the loss of ST wavelets, increase,reach the peak of Tp, and the timing will change, shorten, extend, etc.When ST waves are disordered, it is not easy to calibrate. Fortraditional ECG, this segment it is not easy to measure becausetraditional ECG does not have ST waves and J-point data is notaccurately acquired. This segment is of great significance when the STsegment is not elevated or descended.

T-T Interval

In the traditional ECG waveform, there are only R-R intervals and P-Pintervals. The R-R interval is between first and second heartbeats(ventricular depolarization) and the P-P interval is between first andsecond heartbeats (atrial depolarization). T-T interval 2360 is measuredbetween first and second heartbeats (ventricular repolarization). Sincethere is no atrial repolarization in the traditional ECG waveform, theT-T interval makes sense. These three intervals (P-P interval, R-Rinterval, T-T interval) need to be analyzed and judged at the same time.Alternatively, a P-P interval can be used to compare to a T-T interval,and a P-P interval is compared to an R-R interval to identify changes inthe ventricular depolarization or repolarization. It is more convenientto apply quantitative data values to the Vital Sign Monitoring.

FIG. 24 is an exemplary diagram 2400 showing how heart signal drift isfiltered, in accordance with various embodiments. Signals 2410 showheart signal drift. Signals 2420 show how heart signal drift is improvedby applying an automatic identification anti-drift filter with automaticfrequency selection. Even if data goes out of range, you get a straightline. If data goes out of the lead, you can basically recover andrestore the original signal. As a result of long-time monitoring, theelectrode produces polarization voltage and impedance increases. Thisoccurs especially with a change of the indoor temperature and humidityin the clinical environment in winter. These temperature and humiditychanges can easily produce electrical signal drift.

System for Displaying Intervals and Segments

In various embodiments, artificial intelligence (AI) in conjunction witha database of normal and abnormal ECG data is used to display andmeasure intervals and segments of an ECG waveform during measurement ofthe ECG waveform. This system is referred to as an aiECG system or asystem for performing aiECG, for example.

Returning to FIG. 19, electrodes 1910 are attached to the skin of apatient in a noninvasive measurement, for example. In an alternativeembodiment, electrodes 1910 are attached directly on the surface of abeating heart of a patient. Electrical signals produced by a beatingheart are detected between pairs of electrodes 1910.

A voltage signal is detected between two electrodes 1910 by detector1920. Detector 1920 also amplifies the voltage signal. Detector 1920converts the electrical impulses to an ECG waveform for each heartbeatof the beating heart. Detector 1920 converts the voltage signal to adigital voltage signal using an analog to digital converter (A/D), forexample. Detector 1920 provides the detected and amplified voltagesignal from each pair of electrodes 1910 directly to display device 1940to display the ECG waveform. The ECG waveform includes conventional P,Q, R, S, T, U, and J waveforms, for example. Detector 1920 also providesthe detected and amplified voltage signal from each pair of electrodes1910 directly to processor 1930.

Processor 1930 can be a separate electronic device that can include, butis not limited to, an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), or a general-purpose processor orcomputer, such as the system of FIG. 1. Processor 1930 can be softwareimplemented on another processor of the ECG device, such as a processorof display device 1940. Processor 1930 can also include a remote servercomputer.

Processor 1930 receives the ECG waveform for at least one heartbeat fromdetector 1920. Processor 1930 converts the ECG waveform to a frequencydomain waveform. Processor 1930 separates the frequency domain waveforminto two or more different frequency domain waveforms. Processor 1930converts the two or more different frequency domain waveforms into aplurality of time domain cardiac electrophysiological subwaveforms anddiscontinuity points between these subwaveforms of the ECG waveform.

Processor 1930 compares the plurality of subwaveforms and discontinuitypoints to a database (not shown) of subwaveforms and discontinuitypoints for cardiac electrophysiological signals of ECG waveforms for aplurality of known and normal and abnormal patients or to a set of rulesdeveloped from the database. Processor 1930 identifies a bundle branches(BB) to J-Point (BB-J) interval from the comparison. The set of rulesdeveloped from the database can be, but are not limited to, AI rules ormachine learning rules.

Display device 1940 is an electronic display device, a printer, or anycombination of the two. Display device 1940 displays the ECG waveformwith the BB-J interval annotated for the at least one heartbeat of thebeating heart.

In various embodiments, processor 1930 further calculates a length ofthe BB-J interval in time and display device 1940 further displays thelength of the BB-J interval in time for the at least one heartbeat ofthe beating heart.

In various embodiments, display device 1940 further displays a BB-Jinterval standard value range for the length of the BB-J interval. Ifthe length of the BB-J interval is outside of the BB-J interval standardvalue range, display device 1940 displays the length of the BB-Jinterval in a different color and sounds an audible alarm.

In various embodiments, processor 1930 further identifies a BB to STsegment (B-ST) interval from the comparison and calculates a length ofthe BB-ST interval in time. Display device 1940 further displays the ECGwaveform with the BB-ST interval annotated and displays the length ofthe BB-ST interval in time for the at least one heartbeat of the beatingheart.

In various embodiments, display device 1940 further displays a BB-STinterval standard value range for the length of the BB-ST interval. Ifthe length of the BB-ST interval is outside of the BB-ST intervalstandard value range, display device 1940 displays the length of theBB-ST interval in a different color and sounds an audible alarm.

In various embodiments, processor 1930 further identifies an i-R segmentbetween an initial end of the Purkinje's fibers (I-point) to a maximumpeak of ventricular depolarization (R) from the comparison andcalculates a length of the i-R segment in time. Display device 1940further displays the ECG waveform with the i-R segment annotated anddisplays the length of the i-R segment in time for the at least oneheartbeat of the beating heart.

In various embodiments, display device 1940 further displays an i-Rsegment standard value range for the length of the i-R segment. If thelength of the i-R segment is outside of the i-R segment standard valuerange, display device 1940 displays the length of the i-R segment in adifferent color and sounds an audible alarm.

In various embodiments, processor 1930 further identifies an R-j segmentbetween a maximum peak of ventricular depolarization (R) and a terminalend of the Purkinje's fibers (J-Point) from the comparison andcalculates a length of the R-j segment in time. Display device 1940further displays the ECG waveform with the R-j segment annotated anddisplays the length of the R-j segment in time for the at least oneheartbeat of the beating heart.

In various embodiments, display device 1940 further displays an R-jsegment standard value range for the length of the R-j segment. If thelength of the R-j segment is outside of the R-j segment standard valuerange, display device 1940 displays the length of the R-j segment in adifferent color and sounds an audible alarm.

In various embodiments, processor 1930 further identifies a J-Tp segmentbetween a terminal end of the Purkinje's fibers (J-point) and a maximumpeak of ventricular repolarization (Tp) from the comparison andcalculates a length of the J-Tp segment in time. Display device 1940further displays the ECG waveform with the J-Tp segment annotated anddisplays the length of the J-Tp segment in time for the at least oneheartbeat of the beating heart.

In various embodiments, display device 1940 further displays a J-Tpsegment standard value range for the length of the J-Tp segment. If thelength of the J-Tp segment is outside of the J-Tp segment standard valuerange, display device 1940 displays the length of the J-Tp segment in adifferent color and sounds an audible alarm.

In various embodiments, processor 1930 further identifies a T-T intervalbetween a maximum peak of ventricular repolarization (Tp) of a firstheartbeat and a maximum peak of ventricular repolarization (Tp) of asecond heartbeat from the comparison and calculates a length of the T-Tinterval in time. Display device 1940 further displays the ECG waveformwith the T-T interval annotated and displays the length of the T-Tinterval in time for the at least one heartbeat of the beating heart.

In various embodiments, display device 1940 further displays a T-Tinterval standard value range for the length of the T-T interval. If thelength of the T-T interval is outside of the T-T interval standard valuerange, display device 1940 displays the length of the T-T interval in adifferent color and sounds an audible alarm.

Method for Displaying Intervals and Segments

FIG. 25 is a flowchart showing a method 2500 for displaying andmeasuring intervals and segments of an ECG waveform during measurementof the ECG waveform, in accordance with various embodiments.

In step 2510 of method 2500, electrical impulses are received from abeating heart using two or more electrodes.

In step 2520, the electrical impulses are detected from at least onepair of electrodes of the two or more electrodes and converted to an ECGwaveform for each heartbeat of the beating heart using a detector.

In step 2530, the ECG waveform for at least one heartbeat is receivedfrom the detector, the ECG waveform is converted to a frequency domainwaveform, the frequency domain waveform is separated into two or moredifferent frequency domain waveforms, and the two or more differentfrequency domain waveforms are converted into a plurality of time domaincardiac electrophysiological subwaveforms and discontinuity pointsbetween these subwaveforms of the ECG waveform using a processor.

In step 2540, the plurality of subwaveforms and discontinuity points arecompared to a database of subwaveforms and discontinuity points forcardiac electrophysiological signals of ECG waveforms for a plurality ofknown and normal and abnormal patients or to a set of rules developedfrom the database using the processor.

In step 2550, a bundle branches (BB) to J-Point (BB-J) interval isidentified from the plurality of subwaveforms and discontinuity pointsbased on the comparison using the processor.

In step 2560, the ECG waveform with the BB-J interval annotated isdisplayed for the at least one heartbeat of the beating heart using thedisplay device.

Further, in describing representative embodiments of the presentinvention, the specification may have presented the method and/orprocess of the present invention as a particular sequence of steps.However, to the extent that the method or process does not rely on theparticular order of steps set forth herein, the method or process shouldnot be limited to the particular sequence of steps described. As one ofordinary skill in the art would appreciate, other sequences of steps maybe possible. Therefore, the particular order of the steps set forth inthe specification should not be construed as limitations on the claims.In addition, the claims directed to the method and/or process of thepresent invention should not be limited to the performance of theirsteps in the order written, and one skilled in the art can readilyappreciate that the sequences may be varied and still remain within thespirit and scope of the present invention.

What is claimed is:
 1. A noninvasive electrocardiography (ECG) systemfor displaying and measuring intervals and segments of an ECG waveformduring measurement of the ECG waveform, comprising: two or moreelectrodes adapted to be located near a beating heart of a patient andattached to the skin of the patient that receive electrical impulsesfrom the beating heart; a detector that detects the electrical impulsesfrom at least one pair of electrodes of the two or more electrodes andconverts the electrical impulses to an ECG waveform for each heartbeatof the beating heart; a processor that receives the ECG waveform for atleast one heartbeat from the detector, converts the ECG waveform to afrequency domain waveform, separates the frequency domain waveform intotwo or more different frequency domain waveforms, and converts the twoor more different frequency domain waveforms into a plurality of timedomain cardiac electrophysiological subwaveforms and discontinuitypoints between these subwaveforms of the ECG waveform, compares theplurality of subwaveforms and discontinuity points to a database ofsubwaveforms and discontinuity points for cardiac electrophysiologicalsignals of ECG waveforms for a plurality of known and normal andabnormal patients or to a set of rules developed from the database, andidentifies a bundle branches (BB) to J-Point (BB-J) interval from thecomparison, calculates a length of the BB-J interval in time; and adisplay device that displays the ECG waveform with the BB-J intervalannotated and displays the length of the BB-J interval in time for theat least one heartbeat of the beating heart.
 2. The ECG system of claim1, wherein the display device further displays a BB-J interval standardvalue range for the length of the BB-J interval and, if the length ofthe BB-J interval is outside of the BB-J interval standard value range,displays the length of the BB-J interval in a different color and soundsan audible alarm.
 3. The ECG system of claim 1, wherein the processorfurther identifies a BB to ST segment (B-ST) interval from thecomparison and calculates a length of the BB-ST interval in time andwherein the display device further displays the ECG waveform with theBB-ST interval annotated and displays the length of the BB-ST intervalin time for the at least one heartbeat of the beating heart.
 4. The ECGsystem of claim 3, wherein the display device further displays a BB-STinterval standard value range for the length of the BB-ST interval and,if the length of the BB-ST interval is outside of the BB-ST intervalstandard value range, displays the length of the BB-ST interval in adifferent color and sounds an audible alarm.
 5. The ECG system of claim1, wherein the processor further identifies an i-R segment between aninitial end of the Purkinje's fibers (I-point) to a maximum peak ofventricular depolarization (R) from the comparison and calculates alength of the i-R segment in time and wherein the display device furtherdisplays the ECG waveform with the i-R segment annotated and displaysthe length of the i-R segment in time for the at least one heartbeat ofthe beating heart.
 6. The ECG system of claim 5, wherein the displaydevice further displays an i-R segment standard value range for thelength of the i-R segment and, if the length of the i-R segment isoutside of the i-R segment standard value range, displays the length ofthe i-R segment in a different color and sounds an audible alarm.
 7. TheECG system of claim 1, wherein the processor further identifies an R-jsegment between a maximum peak of ventricular depolarization (R) and aterminal end of the Purkinje's fibers (J-Point) from the comparison andcalculates a length of the R-j segment in time and wherein the displaydevice further displays the ECG waveform with the R-j segment annotatedand displays the length of the R-j segment in time for the at least oneheartbeat of the beating heart.
 8. The ECG system of claim 7, whereinthe display device further displays an R-j segment standard value rangefor the length of the R-j segment and, if the length of the R-j segmentis outside of the R-j segment standard value range, displays the lengthof the R-j segment in a different color and sounds an audible alarm. 9.The ECG system of claim 1, wherein the processor further identifies aJ-Tp segment between a terminal end of the Purkinje's fibers (J-point)and a maximum peak of ventricular repolarization (Tp) from thecomparison and calculates a length of the J-Tp segment in time andwherein the display device further displays the ECG waveform with theJ-Tp segment annotated and displays the length of the J-Tp segment intime for the at least one heartbeat of the beating heart.
 10. The ECGsystem of claim 9, wherein the display device further displays a J-Tpsegment standard value range for the length of the J-Tp segment and, ifthe length of the J-Tp segment is outside of the J-Tp segment standardvalue range, displays the length of the J-Tp segment in a differentcolor and sounds an audible alarm.
 11. The ECG system of claim 1,wherein the processor further identifies a T-T interval between amaximum peak of ventricular repolarization (Tp) of a first heartbeat anda maximum peak of ventricular repolarization (Tp) of a second heartbeatfrom the comparison and calculates a length of the T-T interval in timeand wherein the display device further displays the ECG waveform withthe T-T interval annotated and displays the length of the T-T intervalin time for the at least one heartbeat of the beating heart.
 12. The ECGsystem of claim 11, wherein the display device further displays a T-Tinterval standard value range for the length of the T-T interval and, ifthe length of the T-T interval is outside of the T-T interval standardvalue range, displays the length of the T-T interval in a differentcolor and sounds an audible alarm.
 13. A method for displaying andmeasuring intervals and segments of an electrocardiography (ECG)waveform during measurement of the ECG waveform, comprising: receivingelectrical impulses from a beating heart using two or more electrodes;detecting the electrical impulses from at least one pair of electrodesof the two or more electrodes and converting the electrical impulses toan ECG waveform for each heartbeat of the beating heart using adetector; receiving the ECG waveform for at least one heartbeat from thedetector, converting the ECG waveform to a frequency domain waveform,separating the frequency domain waveform into two or more differentfrequency domain waveforms, and converting the two or more differentfrequency domain waveforms into a plurality of time domain cardiacelectrophysiological subwaveforms and discontinuity points between thesesubwaveforms of the ECG waveform using a processor; comparing theplurality of subwaveforms and discontinuity points to a database ofsubwaveforms and discontinuity points for cardiac electrophysiologicalsignals of ECG waveforms for a plurality of known and normal andabnormal patients or to a set of rules developed from the database usingthe processor; identifying a bundle branches (BB) to J-Point (BB-J)interval from the plurality of subwaveforms and discontinuity pointsbased on the comparison using the processor; and displaying the ECGwaveform with the BB-J interval annotated for the at least one heartbeatof the beating heart using the display device.
 14. The method of claim13, further comprising displaying a BB-J interval standard value rangefor the length of the BB-J interval using the display device and if thelength of the BB-J interval is outside of the BB-J interval standardvalue range, displaying the length of the BB-J interval in a differentcolor and sounding an audible alarm using the display device.
 15. Themethod of claim 13, further comprising identifying a BB to ST segment(B-ST) interval from the comparison using the processor, calculating alength of the BB-ST interval in time using the processor, and displayingthe ECG waveform with the BB-ST interval annotated and displays thelength of the BB-ST interval in time for the at least one heartbeat ofthe beating heart using the display device.
 16. The method of claim 13,further comprising identifying an i-R segment between an initial end ofthe Purkinje's fibers (I-point) to a maximum peak of ventriculardepolarization (R) from the comparison using the processor, calculatinga length of the i-R segment in time using the processor, and displayingthe ECG waveform with the i-R segment annotated and displays the lengthof the i-R segment in time for the at least one heartbeat of the beatingheart using the display device.
 17. The method of claim 13, furthercomprising identifying an R-j segment between a maximum peak ofventricular depolarization (R) and a terminal end of the Purkinje'sfibers (J-Point) from the comparison using the processor, calculating alength of the R-j segment in time using the processor, and displayingthe ECG waveform with the R-j segment annotated and displays the lengthof the R-j segment in time for the at least one heartbeat of the beatingheart using the display device.
 18. The method of claim 13, furthercomprising identifying a J-Tp segment between a terminal end of thePurkinje's fibers (J-point) and a maximum peak of ventricularrepolarization (Tp) from the comparison using the processor, calculatinga length of the J-Tp segment in time using the processor, and displayingthe ECG waveform with the J-Tp segment annotated and displays the lengthof the J-Tp segment in time for the at least one heartbeat of thebeating heart using the display device.
 19. The method of claim 13,further comprising identifying a T-T interval between a maximum peak ofventricular repolarization (Tp) of a first heartbeat and a maximum peakof ventricular repolarization (Tp) of a second heartbeat from thecomparison using the processor, calculating a length of the T-T intervalin time using the processor, and displaying the ECG waveform with theT-T interval annotated and displays the length of the T-T interval intime for the at least one heartbeat of the beating heart using thedisplay device.
 20. An invasive electrocardiography (ECG) system fordisplaying and measuring intervals and segments of an ECG waveformduring measurement of the ECG waveform, comprising: two or moreelectrodes placed directly on the surface of a beating heart of apatient that receive electrical impulses from the beating heart; adetector that detects the electrical impulses from at least one pair ofelectrodes of the two or more electrodes and converts the electricalimpulses to an ECG waveform for each heartbeat of the beating heart; aprocessor that receives the ECG waveform for at least one heartbeat fromthe detector, converts the ECG waveform to a frequency domain waveform,separates the frequency domain waveform into two or more differentfrequency domain waveforms, and converts the two or more differentfrequency domain waveforms into a plurality of time domain cardiacelectrophysiological subwaveforms and discontinuity points between thesesubwaveforms of the ECG waveform, compares the plurality of subwaveformsand discontinuity points to a database of subwaveforms and discontinuitypoints for cardiac electrophysiological signals of ECG waveforms for aplurality of known and normal and abnormal patients or to a set of rulesdeveloped from the database, calculates a length of the BB-J interval intime, and identifies a bundle branches (BB) to J-Point (BB-J) intervalfrom the comparison; and a display device that displays the ECG waveformwith the BB-J interval annotated and displays the length of the BB-Jinterval in time for the at least one heartbeat of the beating heart.